Surgical aid visualization system for glioblastoma tumor identification based on deep learning and in-vivo hyperspectral images of human patients

Himar Fabelo; Martin Halicek; Samuel Ortega; Adam Szolna; Jesus Morera; Roberto Sarmiento; Gustavo M. Callico; Baowei Fei

doi:10.1117/12.2512569

Translator Disclaimer

8 March 2019 Surgical aid visualization system for glioblastoma tumor identification based on deep learning and in-vivo hyperspectral images of human patients

Himar Fabelo , Martin Halicek , Samuel Ortega, Adam Szolna, Jesus Morera, Roberto Sarmiento, Gustavo M. Callico, Baowei Fei

Author Affiliations +

Proceedings Volume 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling; 1095110 (2019) https://doi.org/10.1117/12.2512569
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Brain cancer surgery has the goal of performing an accurate resection of the tumor and preserving as much as possible the quality of life of the patient. There is a clinical need to develop non-invasive techniques that can provide reliable assistance for tumor resection in real-time during surgical procedures. Hyperspectral imaging (HSI) arises as a new, noninvasive and non-ionizing technique that can assist neurosurgeons during this difficult task. In this paper, we explore the use of deep learning (DL) techniques for processing hyperspectral (HS) images of in-vivo human brain tissue. We developed a surgical aid visualization system capable of offering guidance to the operating surgeon to achieve a successful and accurate tumor resection. The employed HS database is composed of 26 in-vivo hypercubes from 16 different human patients, among which 258,810 labelled pixels were used for evaluation. The proposed DL methods achieve an overall accuracy of 95% and 85% for binary and multiclass classifications, respectively. The proposed visualization system is able to generate a classification map that is formed by the combination of the DL map and an unsupervised clustering via a majority voting algorithm. This map can be adjusted by the operating surgeon to find the suitable configuration for the current situation during the surgical procedure.

Conference Presentation

Citation Download Citation

Himar Fabelo , Martin Halicek , Samuel Ortega, Adam Szolna, Jesus Morera, Roberto Sarmiento, Gustavo M. Callico, and Baowei Fei "Surgical aid visualization system for glioblastoma tumor identification based on deep learning and in-vivo hyperspectral images of human patients", Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 1095110 (8 March 2019); https://doi.org/10.1117/12.2512569

ACCESS THE FULL ARTICLE